Spatial and temporal circuit cutting with hypergraphic partitioning

• URL: http://arxiv.org/abs/2504.09334v1
• Date: Sat, 12 Apr 2025 20:31:07 GMT
• Title: Spatial and temporal circuit cutting with hypergraphic partitioning
• Authors: Waldemir Cambiucci, Regina Melo Silveira, Wilson Vicente Ruggiero,
• Abstract summary: This paper presents a hypergraph-based circuit cutting methodology suitable for both spatial and temporal scenarios.<n>By modeling quantum circuits as high-level hypergraphs, we apply partitionings such as Stoer-Wagner, Fiduccia-Mattheyses, and Kernighan-Lin.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Quantum computing promises to revolutionize problem-solving through quantum mechanics, but current NISQ devices face limitations in qubit count and error rates, hindering the execution of large-scale quantum circuits. To address these challenges and improve scalability, two main circuit cutting strategies have emerged: the gate-cut approach, which distributes circuit segments across multiple QPUs (spatial), and the qubit wire cut approach, which divides circuits for sequential execution (temporal). This paper presents a hypergraph-based circuit cutting methodology suitable for both spatial and temporal scenarios. By modeling quantum circuits as high-level hypergraphs, we apply partitioning heuristics such as Stoer-Wagner, Fiduccia-Mattheyses, and Kernighan-Lin to optimize the partitioning process. Our approach aims to reduce communication overhead in spatial cuts and minimize qubit initialization costs in temporal ones. To assess effectiveness, we propose a new evaluation metric called the coupling ratio, which quantifies the trade-offs between communication and initialization. Comparative analyses show that hypergraph partitioning improves the efficiency of distributed quantum architectures. Notably, the Fiduccia-Mattheyses heuristic offers superior performance and adaptability for real-time circuit cutting on multi-QPU systems. Overall, this work positions hypergraph partitioning as a foundational technique for scalable quantum computing in distributed environments.

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